The two-tier architecture approach is a traditional method often used for smaller systems where a client application communicates directly with the database. In a machine learning model, it can be an approach where the data scientist works directly with the data.
In this approach, the data scientist might use languages like Python or R to explore and transform the data, choose an algorithm, train a model, test it, refine it, and ultimately deploy it to a production environment. The programming environment, whether it’s Jupyter notebooks or an Integrated Development Environment (IDE) like Visual Studio, is directly connected to the data source.
On Azure, you might use the Azure Machine Learning Workspace for this approach, where you can manage datasets and compute resources, and write code in notebooks.
2. Three-Tier Architecture Approach:
The more advanced three-tier architecture approach is used when you need to separate the presentation, application processing, and data management functions for more complex systems.
For developing and deploying a machine learning model with Azure, this could mean using either Azure Machine Learning designer or Automated ML.
In Azure Machine Learning designer, there’s no need to write too much code. The designer gives you a visual interface where you drag, drop, and connect modules that can represent each step in the machine learning development pipeline: from accessing and transforming data, choosing and running a machine learning algorithm, training the model, testing it, and deploying it.
Automated ML, on the other hand, gives you an easy way to automate the process of training a model. You provide labeled training data, and Automated ML tests many different combinations of algorithms and hyperparameters to arrive at the best model.
Comparison:
| Approach | Pros | Cons |
|---|---|---|
| Two-tier Architecture | Allows for a deep, customized understanding of the data and model. | Requires more coding and may take longer. |
| Three-tier Architecture | Quicker to implement, does not require extensive coding experience. | Provides less control and understanding of every detail. |
In conclusion, choosing the right development approach to build or train a model depends on numerous factors, including your dataset, the problem you’re trying to solve, your coding abilities, and the resources you have at hand. By understanding these development approaches and how they can be implemented in Azure, you’ll be better equipped for the scenarios in the DP-100 exam.
Practice Test
True/False: Agile development is the least preferred approach for model development in AI/ML due to its flexibility.
- Answer: False
Explanation: Agile development is a commonly preferred approach because it is iterative, flexible and incorporates feedback at several stages. It helps in continuous improvement of the model.
Multiple Select: Which of the following are common development approaches for AI/ML models?
- a) Waterfall
- b) Agile
- c) Prototyping
- d) Peanut butter layering
Answer: a) Waterfall, b) Agile, c) Prototyping
Explanation: Waterfall, Agile, and Prototyping are standard approaches in model development. Peanut butter layering is not a recognized approach.
True/False: The Waterfall development approach allows for changes and improvements in stages once a stage has been completed.
- Answer: False
Explanation: In the Waterfall development approach, each stage is completed fully before moving on to the next, and changes aren’t typically made to a stage once it’s completed.
Multiple Select: Which of the following factors influence the development approach for AI/ML models?
- a) Size of the project
- b) Model complexity
- c) Time constraints
- d) Project budget
Answer: a) Size of the project, b) Model complexity, c) Time constraints, d) Project budget
Explanation: All these factors contribute to the choice of development approach. Deciding on an approach will involve a balance between these aspects.
Single Select: Which of the following development approaches involves building a simplified version of the model first?
- a) Agile
- b) Waterfall
- c) Prototyping
Answer: c) Prototyping
Explanation: The approach of Prototyping involves creating a simplified model, or a prototype, before building the full model.
True/False: Choosing the appropriate development approach should be the first step in model development.
- Answer: True
Explanation: The appropriate development approach should be chosen first as it will influence subsequent decisions in the model building process.
Single Select: Which of the following development approaches is considered more rigid and structured?
- a) Waterfall
- b) Agile
- c) Prototyping
Answer: a) Waterfall
Explanation: Waterfall is a sequential design process, often used in development where the stages are organised in a downward flow, thus it’s considered more rigid and structured.
True/False: In Agile development approach, the model is typically built in small, iterative cycles or sprints.
- Answer: True
Explanation: Agile development is characterized by building the model in small, repetitive sequences or parts, called sprints, which allows frequent evaluation and improvement of the model.
Single Select: Which development approach would be more suitable for a small, time-sensitive AI/ML project?
- a) Waterfall
- b) Agile
- c) Prototyping
Answer: b) Agile
Explanation: Agile development is typically better suited for smaller projects with tight deadlines as it allows for continuous delivery and feedback integration.
True/False: In the prototyping development approach, changes are usually easy to make at any stage of model development.
- Answer: True
Explanation: The defining feature of the prototyping approach is that a simple prototype is built first, which can be easily changed and refined until it transforms into the final model.
Interview Questions
What are the key factors to consider when choosing a model development approach?
Factors to include are the complexity and variety of data, the speed of development requirements, the skill set of the team, the reliability of the solution, and compliance and governance requirements associated with the data and model.
Which Azure service enables data scientists and developers to build, train, and deploy machine learning models?
Azure Machine Learning Service.
When is the automated ML approach the most suitable for building models?
Automated ML is most suitable when you want to quickly build and deploy an ML model and you don’t need a deep understanding of the underlying algorithms or when you need to try multiple algorithms and pre-processing steps to find the best performing model.
Can Azure Machine Learning be used with both Python and R for model development?
Yes, Azure Machine Learning supports both Python and R languages for model development.
In what instance would you typically choose a traditional approach instead of AutoML for building models?
You’d typically choose a traditional approach when you have specialized data processing needs, when you have a very large dataset that requires distributed processing, or when you have complex models that require custom tuning.
Explain the role of Azure Databricks in model development?
Azure Databricks provides a collaborative environment for machine learning model development, especially for large datasets. It provides support for multiple languages and frameworks, and integrates deeply with Azure Machine Learning.
For which purpose would you use Azure Machine Learning designer?
Azure Machine Learning designer is used for drag-and-drop machine learning model development. It’s particularly useful for less technical users or for rapid prototyping.
When may Azure Cognitive Services be the best choice for model development?
Azure Cognitive Services may be the best choice when you need to incorporate pre-trained AI models into your applications, such as for vision, speech, language, and decision services.
What are the advantages of using Azure ML Pipelines for model development and training?
Azure ML Pipelines allow for the creation of workflows that are efficient, reusable and reliable. They enable automation and orchestration of machine learning workflows and simplify the management of machine learning solutions.
Which type of data science projects are most suitable for AutoML?
AutoML is most suitable for projects with well-defined and understood problems where the objective is to quickly identify a model that gives good predictions. It’s less suitable for exploratory projects where the objective is to gain a deeper understanding of the data.
Why might a developer choose to use SDKs instead of Azure Machine Learning Studio for model development?
Developers might opt for SDKs when they require more control over their development environment, need to use programming languages, or want to automate aspects of their workflow.
How does Azure Machine Learning ensure model reproducibility?
Azure Machine Learning tracks all relevant information about your experiment for each model training run, such as code, datasets, hyperparameters, and environment. This can help to reproduce the experiment later.
Why is experiment tracking important in model development?
Experiment tracking is important to track the performance of each model, compare models, and select the best performing model.
What is the function of a compute target in Azure Machine Learning?
A compute target is the compute resource where you run your training script or host your service deployment.
What kind of algorithms does AutoML in Azure Machine Learning support?
AutoML in Azure Machine Learning supports a variety of machine learning models such as classification, regression, time series, and others. It automates the process of selecting the best algorithm and hyperparameters.
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